SWDGAN: GAN-based sampling and whole image denoising network for compressed sensing image reconstruction

Abstract. We propose a framework named sampling and whole image denoising network based on generative adversarial network (GAN) for compressed sensing image reconstruction (SWDGAN) to reconstruct natural image from compressed sensing (CS) measurements. This work is devoted to balancing the performance of reconstruction quality, practicability, and running efficiency. Different from the recent deep learning reconstruction networks, we further enhance the feature representation and remove the blocking artifacts by introducing a whole image dense residual denoising module without affecting running efficiency. To improve the flexibility of sampling process and the practicability of our algorithm, a fully connected network without bias is applied in the sampling process, whose weight can be extracted separately and used as a measurement matrix. In this way, the measurements can be obtained by matrix multiplication in multiple running environments, not limited to deep learning framework. Besides, the sampling network can also improve the reconstruction quality even at low sampling ratios. Moreover, we remove batch normalization (BN) layer of reconstruction network to avoid the influence of BN artifact on reconstruction image. The experimental results illustrate that our method outperforms the most advanced traditional methods and deep learning-based methods in terms of reconstruction quality and running time.